where \(_t^a \in (0,1) \)  is the backtracking step-size parameter which controls the line search for guaranteeing the conjugate gradient improvement given the KL divergence constraint. It is desirable to set up a fairly large step size \( _t^a\)  to initialize the line search space on the policy, and gradually shrink \(_t^a\)  until a Armijo-Goldstein condition [1]} is satisfied where a critical (optimal) point is obtained. blackThe impact of such parameters on the learning performance will be investigated in Section REF .
